Synchronization and nonlinear modulation methods for OFDM-based wireless communications
Bibliographic record
Abstract
QAM modulation of OFDM signals (OFDM-QAM) is a popular modulation scheme for linear communication systems because it achieves good spectral efficiency while simplifying the equalization process. This comes at the cost of requiring accurate synchronization methods in the receiver, highly stable oscillators (low phase-noise); and power inefficient transmission. This work addresses issues in these three areas. First, a new frame synchronization method is developed based on Bayesian changepoint principles. It has higher accuracy then current popular methods, can be implemented recursively, and has been tested with success on experimental data collected in a WLAN testbed. Second, phase-noise resistant communication is investigated by performing an accurate statistical analysis of self-heterodyne communication systems. The analysis can be used to accurately predict the bit error probability of such systems. Third, power efficient transmission using phase-modulation (OFDM-PM) is analyzed. The bandwidth, equivalent baseband model, and symbol error probability are investigated. We can now predict the performance of OFDM-PM systems; for a given spectral efficiency, when operating well into the threshold region of angle-demodulators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".